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Lab: Fine-Tune a Binary Classification Model
Use Case: Medical Fraud
On this mission, you’ll learn how an imbalanced dataset can affect a binary classification model’s performance, in this case, a model that predicts the likelihood of medical fraud. You’ll optimize the model based on the estimated cost of the fraud versus the cost of auditing a provider. You will also investigate how a rules-based approach can segment the data using a RuleFit classifier model.
Mission format and duration: self-paced, hands-on, 1 hour
Upon completion of this mission, you will be able to:
- Build and evaluate a binary classification model that predicts medical fraud
- Perform a profit-loss analysis using the Profit Curve tool to optimize the predictive threshold of your model
- Use the Hot Spots tool with a RuleFit classifier model to understand how a rules-based approach would segment the data
- Use the Word Cloud tool to understand the relationship between the medications prescribed and the likelihood of a provider perpetrating fraud
- Make predictions based on the model
Who should complete this mission?
- Business Analysts
- Citizen Data Scientists
- Data Scientists
Before embarking on this mission, you should complete one of the following:
- Starter Quest appropriate for your role (self-paced)
- AutoML I (virtual instructor-led mission)
- DataRobot for Data Scientists (virtual instructor-led mission)
- Chrome browser
- DataRobot Automated Machine Learning — If you don’t have access to the application, please sign up for our free trial: datarobot.com/trial.